OpenRaiser/Intern-Atlas huggingface dataset:
A Framework for Implementing Machine Learning on Omics Data
Venue: arXiv cs.LG 2018
Year: 2018

The potential benefits of applying machine learning methods to -omics data are becoming increasingly apparent, especially in clinical settings. However, the unique characteristics of these data are not always well suited to machine learning techniques. These data are often generated across different technologies in different labs, and frequently with high dimensionality. In this paper we present a framework for combining -omics data sets, and for handling high dimensional data, making -omics research more accessible to machine learning applications. We demonstrate the success of this framework through integration and analysis of multi-analyte data for a set of 3,533 breast cancers. We then use this data-set to predict breast cancer patient survival for individuals at risk of an impending event, with higher accuracy and lower variance than methods trained on individual data-sets. We hope that our pipelines for data-set generation and transformation will open up -omics data to machine learning researchers. We have made these freely available for noncommercial use at www.ccg.ai.
Analysis of large sparse graphs using regular decomposition of graph distance matrices
Venue: arXiv cs.DS 2018
Year: 2018

Statistical analysis of large and sparse graphs is a challenging problem in data science due to the high dimensionality and nonlinearity of the problem. This paper presents a fast and scalable algorithm for partitioning such graphs into disjoint groups based on observed graph distances from a set of reference nodes. The resulting partition provides a low-dimensional approximation of the full distance matrix which helps to reveal global structural properties of the graph using only small samples of the distance matrix. The presented algorithm is inspired by the information-theoretic minimum description principle. We investigate the performance of this algorithm for selected real data sets and for synthetic graph data sets generated using stochastic block models and power-law random graphs, together with analytical considerations for sparse stochastic block models with bounded average degrees.
Auctioning Electricity under Deep Renewable Integration using a Penalty for Shortfall
Venue: arXiv cs.SY 2018
Year: 2018

We analyze the problem of a renewable generator who wants to sell its random generation in a two-stage market to a number of strategic, but flexible, load serving entities (LSEs). To offer an incentive to participate in the auction, the generator promises to pay a penalty associated with any shortfall in generation due to the uncertain nature of the traded resource. We devise an auction mechanism that efficiently allocates electricity among LSEs while eliciting their true valuations. We show that the consumer surplus of the LSEs and expected profit of the generator are positive, thereby creating a win-win situation for all market participants.
Challenges in the Automatic Analysis of Students' Diagnostic Reasoning
Venue: arXiv cs.AI 2018
Year: 2018

Diagnostic reasoning is a key component of many professions. To improve students' diagnostic reasoning skills, educational psychologists analyse and give feedback on epistemic activities used by these students while diagnosing, in particular, hypothesis generation, evidence generation, evidence evaluation, and drawing conclusions. However, this manual analysis is highly time-consuming. We aim to enable the large-scale adoption of diagnostic reasoning analysis and feedback by automating the epistemic activity identification. We create the first corpus for this task, comprising diagnostic reasoning self-explanations of students from two domains annotated with epistemic activities. Based on insights from the corpus creation and the task's characteristics, we discuss three challenges for the automatic identification of epistemic activities using AI methods: the correct identification of epistemic activity spans, the reliable distinction of similar epistemic activities, and the detection of overlapping epistemic activities. We propose a separate performance metric for each challenge and thus provide an evaluation framework for future research. Indeed, our evaluation of various state-of-the-art recurrent neural network architectures reveals that current techniques fail to address some of these challenges.
Grammar-based Representation and Identification of Dynamical Systems
Venue: arXiv cs.SY 2018
Year: 2018

In this paper we propose a novel approach to identify dynamical systems. The method estimates the model structure and the parameters of the model simultaneously, automating the critical decisions involved in identification such as model structure and complexity selection. In order to solve the combined model structure and model parameter estimation problem, a new representation of dynamical systems is proposed. The proposed representation is based on Tree Adjoining Grammar, a formalism that was developed from linguistic considerations. Using the proposed representation, the identification problem can be interpreted as a multi-objective optimization problem and we propose a Evolutionary Algorithm-based approach to solve the problem. A benchmark example is used to demonstrate the proposed approach. The results were found to be comparable to that obtained by state-of-the-art non-linear system identification methods, without making use of knowledge of the system description.
HELOC Applicant Risk Performance Evaluation by Topological Hierarchical Decomposition
Venue: arXiv cs.LG 2018
Year: 2018

Strong regulations in the financial industry mean that any decisions based on machine learning need to be explained. This precludes the use of powerful supervised techniques such as neural networks. In this study we propose a new unsupervised and semi-supervised technique known as the topological hierarchical decomposition (THD). This process breaks a dataset down into ever smaller groups, where groups are associated with a simplicial complex that approximate the underlying topology of a dataset. We apply THD to the FICO machine learning challenge dataset, consisting of anonymized home equity loan applications using the MAPPER algorithm to build simplicial complexes. We identify different groups of individuals unable to pay back loans, and illustrate how the distribution of feature values in a simplicial complex can be used to explain the decision to grant or deny a loan by extracting illustrative explanations from two THDs on the dataset.
Exact Penalization of Generalized Nash Equilibrium Problems
Venue: arXiv cs.GT 2018
Year: 2018

This paper presents an exact penalization theory of the generalized Nash equilibrium problem (GNEP) that has its origin from the renowned Arrow-Debreu general economic equilibrium model. While the latter model is the foundation of much of mathematical economics, the GNEP provides a mathematical model of multi-agent non-cooperative competition that has found many contemporary applications in diverse engineering domains. The most salient feature of the GNEP that distinguishes it from a standard non-cooperative (Nash) game is that each player's optimization problem contains constraints that couple all players' decision variables. Extending results for stand-alone optimization problems, the penalization theory aims to convert the GNEP into a game of the standard kind without the coupled constraints, which is known to be more readily amenable to solution methods and analysis. Starting with an illustrative example to motivate the development, the paper focuses on two kinds of coupled constraints, shared (i.e., common) and finitely representable. Constraint residual functions and the associated error bound theory play an important role throughout the development.
A Unified Coded Deep Neural Network Training Strategy Based on Generalized PolyDot Codes for Matrix Multiplication
Venue: arXiv cs.IT 2018
Year: 2018

This paper has two contributions. First, we propose a novel coded matrix multiplication technique called Generalized PolyDot codes that advances on existing methods for coded matrix multiplication under storage and communication constraints. This technique uses "garbage alignment," i.e., aligning computations in coded computing that are not a part of the desired output. Generalized PolyDot codes bridge between Polynomial codes and MatDot codes, trading off between recovery threshold and communication costs. Second, we demonstrate that Generalized PolyDot can be used for training large Deep Neural Networks (DNNs) on unreliable nodes prone to soft-errors. This requires us to address three additional challenges: (i) prohibitively large overhead of coding the weight matrices in each layer of the DNN at each iteration; (ii) nonlinear operations during training, which are incompatible with linear coding; and (iii) not assuming presence of an error-free master node, requiring us to architect a fully decentralized implementation without any "single point of failure." We allow all primary DNN training steps, namely, matrix multiplication, nonlinear activation, Hadamard product, and update steps as well as the encoding/decoding to be error-prone. We consider the case of mini-batch size $B=1$, as well as $B>1$, leveraging coded matrix-vector products, and matrix-matrix products respectively. The problem of DNN training under soft-errors also motivates an interesting, probabilistic error model under which a real number $(P,Q)$ MDS code is shown to correct $P-Q-1$ errors with probability $1$ as compared to $\lfloor \frac{P-Q}{2} \rfloor$ for the more conventional, adversarial error model. We also demonstrate that our proposed strategy can provide unbounded gains in error tolerance over a competing replication strategy and a preliminary MDS-code-based strategy for both these error models.
Perceptual Conditional Generative Adversarial Networks for End-to-End Image Colourization
Venue: arXiv cs.CV 2018
Year: 2018

Colours are everywhere. They embody a significant part of human visual perception. In this paper, we explore the paradigm of hallucinating colours from a given gray-scale image. The problem of colourization has been dealt in previous literature but mostly in a supervised manner involving user-interference. With the emergence of Deep Learning methods numerous tasks related to computer vision and pattern recognition have been automatized and carried in an end-to-end fashion due to the availability of large data-sets and high-power computing systems. We investigate and build upon the recent success of Conditional Generative Adversarial Networks (cGANs) for Image-to-Image translations. In addition to using the training scheme in the basic cGAN, we propose an encoder-decoder generator network which utilizes the class-specific cross-entropy loss as well as the perceptual loss in addition to the original objective function of cGAN. We train our model on a large-scale dataset and present illustrative qualitative and quantitative analysis of our results. Our results vividly display the versatility and proficiency of our methods through life-like colourization outcomes.
Movie Recommendation System using Sentiment Analysis from Microblogging Data
Venue: arXiv cs.IR 2018
Year: 2018

Recommendation systems are important intelligent systems that play a vital role in providing selective information to users. Traditional approaches in recommendation systems include collaborative filtering and content-based filtering. However, these approaches have certain limitations like the necessity of prior user history and habits for performing the task of recommendation. In order to reduce the effect of such dependencies, this paper proposes a hybrid recommendation system which combines the collaborative filtering, content-based filtering with sentiment analysis of movie tweets. The movie tweets have been collected from microblogging websites to understand the current trends and user response of the movie. Experiments conducted on public database produce promising results.
Noise-tolerant Audio-visual Online Person Verification using an Attention-based Neural Network Fusion
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we present a multi-modal online person verification system using both speech and visual signals. Inspired by neuroscientific findings on the association of voice and face, we propose an attention-based end-to-end neural network that learns multi-sensory associations for the task of person verification. The attention mechanism in our proposed network learns to conditionally select a salient modality between speech and facial representations that provides a balance between complementary inputs. By virtue of this capability, the network is robust to missing or corrupted data from either modality. In the VoxCeleb2 dataset, we show that our method performs favorably against competing multi-modal methods. Even for extreme cases of large corruption or an entirely missing modality, our method demonstrates robustness over other unimodal methods.
AsmetaF: A Flattener for the ASMETA Framework
Venue: arXiv cs.SE 2018
Year: 2018

Abstract State Machines (ASMs) have shown to be a suitable high-level specification method for complex, even industrial, systems; the ASMETA framework, supporting several validation and verification activities on ASM models, is an example of a formal integrated development environment. Although ASMs allow modeling complex systems in a rather concise way -and this is advantageous for specification purposes-, such concise notation is in general a problem for verification activities as model checking and theorem proving that rely on tools accepting simpler notations. In this paper, we propose a flattener tool integrated in the ASMETA framework that transforms a general ASM model in a flattened model constituted only of update, parallel, and conditional rules; such model is easier to map to notations of verification tools. Experiments show the effect of applying the tool to some representative case studies of the ASMETA repository.
Improving the Visualization of Alloy Instances
Venue: arXiv cs.HC 2018
Year: 2018

Alloy is a lightweight formal specification language, supported by an IDE, which has proven well-suited for reasoning about software design in early development stages. The IDE provides a visualizer that produces graphical representations of analysis results, which is essential for the proper validation of the model. Alloy is a rich language but inherently static, so behavior needs to be explicitly encoded and reasoned about. Even though this is a common scenario, the visualizer presents limitations when dealing with such models. The main contribution of this paper is a principled approach to generate instance visualizations, which improves the current Alloy Visualizer, focusing on the representation of behavior.
Finite-time Heterogeneous Cyclic Pursuit with Application to Target Interception
Venue: arXiv cs.SY 2018
Year: 2018

This paper presents a finite-time heterogeneous cyclic pursuit scheme that ensures consensus among agents modelled as integrators. It is shown that for the proposed sliding mode control, even when the gains corresponding to each agent are non-identical, consensus results within a finite-time provided all the gains are positive. An algorithm is presented to compute the consensus value and consensus time for a given set of gains and initial states of the agents. The set of values where consensus can occur, by varying the gains, has been derived and a second algorithm aids in determining the gains that enable consensus at any point in the aforementioned set, within a given finite-time. As an application, the finite-time consensus in line-of-sight (LOS) rates, over a cycle digraph, for a group of interceptors is shown to be effective in ensuring co-operative collision-free interception of a target, for both kinematic and realistic models of the interceptors. Simulations vindicate the theoretical results.
Data Management in Time-Domain Astronomy: Requirements and Challenges
Venue: arXiv cs.DB 2018
Year: 2018

In time-domain astronomy, we need to use the relational database to manage star catalog data. With the development of sky survey technology, the size of star catalog data is larger, and the speed of data generation is faster. So, in this paper, we make a systematic and comprehensive introduction to process the data in time-domain astronomy, and valuable research questions are detailed. Then, we list candidate systems usually used in astronomy and point out the advantages and disadvantages of these systems. In addition, we present the key techniques needed to deal with astronomical data. Finally, we summarize the challenges faced by the design of our database prototype.
Sapiens Chain: A Blockchain-based Cybersecurity Framework
Venue: arXiv cs.CR 2018
Year: 2018

Recently, cybersecurity becomes more and more important due to the rapid development of Internet. However, existing methods are in reality highly sensitive to attacks and are far more vulnerable than expected, as they are lack of trustable measures. In this paper, to address the aforementioned problems, we propose a blockchain-based cybersecurity framework, termed as Sapiens Chain, which can protect the privacy of the anonymous users and ensure that the transactions are immutable by providing decentralized and trustable services. Integrating semantic analysis, symbolic execution, and routing learning methods into intelligent auditing, this framework can achieve good accuracy for detecting hidden vulnerabilities. In addition, a revenue incentive mechanism, which aims to donate participants, is built. The practical results demonstrate the effectiveness of the proposed framework.
MagicVO: End-to-End Monocular Visual Odometry through Deep Bi-directional Recurrent Convolutional Neural Network
Venue: arXiv cs.CV 2018
Year: 2018

This paper proposes a new framework to solve the problem of monocular visual odometry, called MagicVO . Based on Convolutional Neural Network (CNN) and Bi-directional LSTM (Bi-LSTM), MagicVO outputs a 6-DoF absolute-scale pose at each position of the camera with a sequence of continuous monocular images as input. It not only utilizes the outstanding performance of CNN in image feature processing to extract the rich features of image frames fully but also learns the geometric relationship from image sequences pre and post through Bi-LSTM to get a more accurate prediction. A pipeline of the MagicVO is shown in Fig. 1. The MagicVO system is end-to-end, and the results of experiments on the KITTI dataset and the ETH-asl cla dataset show that MagicVO has a better performance than traditional visual odometry (VO) systems in the accuracy of pose and the generalization ability.
Eliminating Exposure Bias and Loss-Evaluation Mismatch in Multiple Object Tracking
Venue: arXiv cs.CV 2018
Year: 2018

Identity Switching remains one of the main difficulties Multiple Object Tracking (MOT) algorithms have to deal with. Many state-of-the-art approaches now use sequence models to solve this problem but their training can be affected by biases that decrease their efficiency. In this paper, we introduce a new training procedure that confronts the algorithm to its own mistakes while explicitly attempting to minimize the number of switches, which results in better training. We propose an iterative scheme of building a rich training set and using it to learn a scoring function that is an explicit proxy for the target tracking metric. Whether using only simple geometric features or more sophisticated ones that also take appearance into account, our approach outperforms the state-of-the-art on several MOT benchmarks.
Correcting the Common Discourse Bias in Linear Representation of Sentences using Conceptors
Venue: arXiv cs.CL 2018
Year: 2018

Distributed representations of words, better known as word embeddings, have become important building blocks for natural language processing tasks. Numerous studies are devoted to transferring the success of unsupervised word embeddings to sentence embeddings. In this paper, we introduce a simple representation of sentences in which a sentence embedding is represented as a weighted average of word vectors followed by a soft projection. We demonstrate the effectiveness of this proposed method on the clinical semantic textual similarity task of the BioCreative/OHNLP Challenge 2018.
Developing Mobility and Traffic Visualization Applications for Connected Vehicles
Venue: arXiv cs.NI 2018
Year: 2018

This technical report is a catalog of two applications that have been enhanced and developed to augment vehicular networking research. The first application is already described in our previous work [21], while the second one is a desktop application that was developed as a publicly-hosted web app which allows any Internet-connected device to remotely monitor a roadway intersection's state over HTTP. This collaborative work was completed under and for the utility of ETSU's Vehicular Networking Lab. It can serve as a basis for further development in the field of connected and autonomous vehicles.
Combining Deep Learning and Qualitative Spatial Reasoning to Learn Complex Structures from Sparse Examples with Noise
Venue: arXiv cs.AI 2018
Year: 2018

Many modern machine learning approaches require vast amounts of training data to learn new concepts; conversely, human learning often requires few examples--sometimes only one--from which the learner can abstract structural concepts. We present a novel approach to introducing new spatial structures to an AI agent, combining deep learning over qualitative spatial relations with various heuristic search algorithms. The agent extracts spatial relations from a sparse set of noisy examples of block-based structures, and trains convolutional and sequential models of those relation sets. To create novel examples of similar structures, the agent begins placing blocks on a virtual table, uses a CNN to predict the most similar complete example structure after each placement, an LSTM to predict the most likely set of remaining moves needed to complete it, and recommends one using heuristic search. We verify that the agent learned the concept by observing its virtual block-building activities, wherein it ranks each potential subsequent action toward building its learned concept. We empirically assess this approach with human participants' ratings of the block structures. Initial results and qualitative evaluations of structures generated by the trained agent show where it has generalized concepts from the training data, which heuristics perform best within the search space, and how we might improve learning and execution.
SOC: hunting the underground inside story of the ethereum Social-network Opinion and Comment
Venue: arXiv cs.CL 2018
Year: 2018

The cryptocurrency is attracting more and more attention because of the blockchain technology. Ethereum is gaining a significant popularity in blockchain community, mainly due to the fact that it is designed in a way that enables developers to write smart contracts and decentralized applications (Dapps). There are many kinds of cryptocurrency information on the social network. The risks and fraud problems behind it have pushed many countries including the United States, South Korea, and China to make warnings and set up corresponding regulations. However, the security of Ethereum smart contracts has not gained much attention. Through the Deep Learning approach, we propose a method of sentiment analysis for Ethereum's community comments. In this research, we first collected the users' cryptocurrency comments from the social network and then fed to our LSTM + CNN model for training. Then we made prediction through sentiment analysis. With our research result, we have demonstrated that both the precision and the recall of sentiment analysis can achieve 0.80+. More importantly, we deploy our sentiment analysis1 on RatingToken and Coin Master (mobile application of Cheetah Mobile Blockchain Security Center23). We can effectively provide detail information to resolve the risks of being fake and fraud problems.